Integrating bulk and single-cell RNA sequencing data reveals epithelial-mesenchymal transition molecular subtype and signature to predict prognosis, immunotherapy efficacy, and drug candidates in low-grade gliomas

Abstract

Objective: Epithelial-mesenchymal transition (EMT) is a tightly regulated and dynamic process occurring in both embryonic development and tumor progression. Our study aimed to comprehensively explore the molecular subtypes, immune landscape, and prognostic signature based on EMT-related genes in low-grade gliomas (LGG) in order to facilitate treatment decision-making and drug discovery.Methods: We curated EMT-related genes and performed molecular subtyping with consensus clustering algorithm to determine EMT expression patterns in LGG. The infiltration level of diverse immune cell subsets was evaluated by implementing the single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithms. The distinctions in clinical characteristics, mutation landscape, and immune tumor microenvironment (TME) among the subtypes were subjected to further investigation. Gene Set Variation Analysis (GSVA) was performed to explore the biological pathways that were involved in subtypes. The chemo drug sensitivity and immunotherapy of subtypes were estimated through GDSC database and NTP algorithm. To detect EMT subtype-related prognostic gene modules, the analysis of weighted gene co-expression network (WGCNA) was performed. The LASSO algorithm was utilized to construct a prognostic risk model, and its efficacy was verified through an independent CGGA dataset. Finally, the expression of the hub genes from the prognostic model was evaluated through the single-cell dataset and in-vitro experiment.Results: The TCGA-LGG dataset revealed the creation of two molecular subtypes that presented different prognoses, clinical implications, TME, mutation landscapes, chemotherapy, and immunotherapy. A three-gene signature (SLC39A1, CTSA and CLIC1) based on EMT expression pattern were established through WGCNA analysis. Low-risk patients showed a positive outlook, increased immune cell presence, and higher expression of immune checkpoint proteins. In addition, several promising drugs, including birinapant, fluvastatin, clofarabine, dasatinib, tanespimycin, TAK−733, GDC−0152, AZD8330, trametinib and ingenol-mebutate had great potential to the treatment of high risk patients. Finally, CTSA and CLIC1 were highly expressed in monocyte cell through single-cell RNA sequencing analysis.Conclusion: Our research revealed non-negligible role of EMT in the TME diversity and complexity of LGG. A prognostic signature may contribute to the personalized treatment and prognostic determination.

Epithelial–Mesenchymal Transition Related Score Functions as a Predictive Tool for Immunotherapy and Candidate Drugs in Glioma

“…Previous studies have shown that EMT can regulate immune escape mechanisms, affecting immune cell infiltration and immune checkpoint molecule expression. 12,13,16 The integration of EMT dynamics into predictive models for glioma risk and prognosis has remained largely unexplored despite its potential to offer a more nuanced and precise understanding of glioma behavior. In this article, we illuminate the promise of EMT-based risk scoring as a robust predictive model for the treatment of gliomas.…”

Expression, Prognostic Value, and Biological Function of CTHRC1 in Different Types of Gliomas: A Bioinformatic Analysis and Experiment Validation

“… 4 - 6 Studies have found a correlation between glioma progression and epithelial-mesenchymal transition (EMT), which promotes tumor proliferation, invasion, and treatment resistance. 7 - 9 Therefore, the identification of EMT-related genes associated with glioma invasion is of significant importance for the clinical treatment of gliomas.…”